Customer Solution Architect — Arango AI Product Suite

Team: IT

Location: India

Commitment: Full-time

Workplace Type: remote

Accountabilities

• Own the technical customer relationship throughout the full lifecycle, from discovery and solution design through pilots, production deployment, and expansion.
• Lead discovery sessions with executives, domain experts, and technical teams to identify high-value AI use cases and connect proposed solutions to measurable business outcomes.
• Design target architectures using multimodel data platforms, including graph schemas, query and traversal patterns, knowledge graphs, and GraphRAG retrieval strategies.
• Define success criteria, SLAs/SLOs, governance requirements, data access controls, and phased delivery plans that support a smooth path from proof of value to production.
• Build reference implementations and prototypes covering graph models, data connectors, GraphRAG pipelines, APIs, tool calling, and AI agent orchestration.
• Guide customers through secure and observable production deployments, including CI/CD, infrastructure-as-code, testing, monitoring, and operational readiness.
• Architect hybrid retrieval solutions combining graph traversal, vector search, embeddings, chunking, ranking, caching, and reranking techniques.
• Establish evaluation frameworks for AI solutions and continuously improve prompts, models, retrieval strategies, and graph structures using relevant metrics and testing approaches.
• Design data pipelines, graph ingestion processes, vector indexes, and metadata governance frameworks to support reliable AI applications.
• Implement monitoring and alerting for quality, model drift, hallucinations, guardrail events, latency, performance, and cost.
• Advise customers on security and compliance requirements, including access controls, secrets management, audit logging, PII protection, and applicable regulatory standards.
• Produce architecture documentation, operational runbooks, reusable implementation patterns, and training materials while enabling customer teams to operate solutions independently.
• Serve as a voice of the customer internally, sharing field insights and technical requirements with product and engineering teams to influence future solutions.

Requirements

• 5+ years of professional experience in software engineering, solution architecture, or technical professional services, including experience building and operating production systems.
• Deep hands-on expertise in graph data modeling, graph queries and traversal, graph algorithms, knowledge graphs, and graph-based AI applications, with direct experience building GraphRAG or knowledge-graph-backed retrieval systems for LLM applications.
• Strong Python and applied AI skills, along with a solid understanding of data structures, systems design, concurrency, and networking.
• Experience working with graph, NoSQL, key-value, and document databases, with multi-model database experience considered valuable.
• Hands-on experience with modern LLMs and AI frameworks such as OpenAI, Anthropic, Llama, Hugging Face, LangChain, LlamaIndex, function calling, and tool calling.
• Strong knowledge of retrieval and vector technologies such as FAISS, pgvector, Pinecone, Weaviate, or comparable solutions, including hybrid graph-and-vector retrieval.
• Experience with cloud and container technologies such as AWS, GCP, or Azure, along with Docker, Kubernetes, Terraform or CloudFormation, and CI/CD practices.
• Familiarity with observability practices, including metrics, logs, traces, monitoring, and performance optimization for latency-sensitive systems.
• Strong understanding of search and information retrieval concepts such as BM25, hybrid retrieval, reranking, ColBERT, or cross-encoders is advantageous.
• Experience with front-end or full-stack technologies such as TypeScript, React, or Next.js for lightweight prototyping is a plus.
• Familiarity with MLOps and evaluation tools such as MLflow, Weights & Biases, Ragas, promptfoo, or DeepEval is beneficial.
• Excellent customer-facing communication skills, with the ability to lead technical discussions with senior executives as well as hands-on engineering teams.
• Strong problem-solving, collaboration, and consulting skills, with the ability to translate complex technical concepts into practical business outcomes.
• Experience in finance, healthcare, public sector, manufacturing, or retail is advantageous, as is familiarity with security, compliance, data residency, and private networking requirements.

Benefits

• Opportunity to work on cutting-edge AI, graph, and contextual data infrastructure.
• Senior-level exposure to enterprise customers and complex AI transformation initiatives.
• Opportunity to influence product and engineering roadmaps through direct customer insights.
• Exposure to modern AI, retrieval, MLOps, cloud, infrastructure, and observability technologies.
• Remote work environment with collaboration across experienced engineering, product, and business teams.
• Opportunity to develop reusable architectures and solutions that can influence broader customer deployments.
• Meaningful role in helping organizations move AI applications from experimentation to reliable production systems.